Iceberg Connector#
Overview#
The Iceberg connector allows querying data stored in Iceberg tables.
Metastores#
Iceberg tables store most of the metadata in the metadata files, along with the data on the
filesystem, but it still requires a central place to find the current location of the
current metadata pointer for a table. This central place is called the Iceberg Catalog.
The Presto Iceberg connector supports different types of Iceberg Catalogs : Hive Metastore,
GLUE, NESSIE, and HADOOP.
To configure the Iceberg connector, create a catalog properties file
etc/catalog/iceberg.properties. To define the catalog type, iceberg.catalog.type property
is required along with the following contents, with the property values replaced as follows:
Hive Metastore catalog#
The Iceberg connector supports the same configuration for HMS as a Hive connector.
connector.name=iceberg
hive.metastore.uri=hostname:port
iceberg.catalog.type=hive
Glue catalog#
The Iceberg connector supports the same configuration for Glue as a Hive connector.
connector.name=iceberg
hive.metastore=glue
iceberg.catalog.type=hive
Nessie catalog#
To use a Nessie catalog, configure the catalog type as
iceberg.catalog.type=nessie
connector.name=iceberg
iceberg.catalog.type=nessie
iceberg.catalog.warehouse=/tmp
iceberg.nessie.uri=https://localhost:19120/api/v1
Additional supported properties for the Nessie catalog:
Property Name |
Description |
|---|---|
|
The branch/tag to use for Nessie, defaults to |
|
Nessie API endpoint URI (required).
Example: |
|
The authentication type to use.
Available values are |
|
The username to use with |
|
The password to use with |
|
The token to use with |
|
The read timeout in milliseconds for requests
to the Nessie server.
Example: |
|
The connection timeout in milliseconds for the connection
requests to the Nessie server.
Example: |
|
Configuration of whether compression should be enabled or
not for requests to the Nessie server, defaults to |
|
Configuration of the custom ClientBuilder implementation class to be used. |
Setting Up Nessie With Docker#
To set up a Nessie instance locally using the Docker image, see Setting up Nessie. Once the Docker instance is up and running, you should see logs similar to the following example:
2023-09-05 13:11:37,905 INFO [io.quarkus] (main) nessie-quarkus 0.69.0 on JVM (powered by Quarkus 3.2.4.Final) started in 1.921s. Listening on: http://0.0.0.0:19120
2023-09-05 13:11:37,906 INFO [io.quarkus] (main) Profile prod activated.
2023-09-05 13:11:37,906 INFO [io.quarkus] (main) Installed features: [agroal, amazon-dynamodb, cassandra-client, cdi, google-cloud-bigtable, hibernate-validator, jdbc-postgresql, logging-sentry, micrometer, mongodb-client, narayana-jta, oidc, opentelemetry, reactive-routes, resteasy, resteasy-jackson, security, security-properties-file, smallrye-context-propagation, smallrye-health, smallrye-openapi, swagger-ui, vertx]
If log messages related to Nessie’s OpenTelemetry collector appear similar to the following example, you can disable OpenTelemetry using the configuration option quarkus.otel.sdk.disabled=true.
2023-08-27 11:10:02,492 INFO [io.qua.htt.access-log] (executor-thread-1) 172.17.0.1 - - [27/Aug/2023:11:10:02 +0000] "GET /api/v1/config HTTP/1.1" 200 62
2023-08-27 11:10:05,007 SEVERE [io.ope.exp.int.grp.OkHttpGrpcExporter] (OkHttp http://localhost:4317/...) Failed to export spans. The request could not be executed. Full error message: Failed to connect to localhost/127.0.0.1:4317
For example, start the Docker image using the following command:
docker run -p 19120:19120 -e QUARKUS_OTEL_SDK_DISABLED=true ghcr.io/projectnessie/nessie
For more information about this configuration option and other related options, see the OpenTelemetry Configuration Reference.
For more information about troubleshooting OpenTelemetry traces, see Troubleshooting traces.
If an error similar to the following example is displayed, this is probably because you are interacting with an http server, and not https server. You need to set iceberg.nessie.uri to http://localhost:19120/api/v1.
Caused by: javax.net.ssl.SSLException: Unsupported or unrecognized SSL message
at sun.security.ssl.SSLSocketInputRecord.handleUnknownRecord(SSLSocketInputRecord.java:448)
at sun.security.ssl.SSLSocketInputRecord.decode(SSLSocketInputRecord.java:174)
at sun.security.ssl.SSLTransport.decode(SSLTransport.java:111)
at sun.security.ssl.SSLSocketImpl.decode(SSLSocketImpl.java:1320)
at sun.security.ssl.SSLSocketImpl.readHandshakeRecord(SSLSocketImpl.java:1233)
at sun.security.ssl.SSLSocketImpl.startHandshake(SSLSocketImpl.java:417)
at sun.security.ssl.SSLSocketImpl.startHandshake(SSLSocketImpl.java:389)
at sun.net.www.protocol.https.HttpsClient.afterConnect(HttpsClient.java:558)
at sun.net.www.protocol.https.AbstractDelegateHttpsURLConnection.connect(AbstractDelegateHttpsURLConnection.java:201)
at sun.net.www.protocol.https.HttpsURLConnectionImpl.connect(HttpsURLConnectionImpl.java:167)
at org.projectnessie.client.http.impl.jdk8.UrlConnectionRequest.executeRequest(UrlConnectionRequest.java:71)
... 42 more
Hadoop catalog#
Iceberg connector supports Hadoop catalog
connector.name=iceberg
iceberg.catalog.type=hadoop
iceberg.catalog.warehouse=hdfs://hostname:port
Configuration Properties#
Note
The Iceberg connector supports configuration options for Amazon S3 as a Hive connector.
The following configuration properties are available:
Property Name |
Description |
Default |
|---|---|---|
|
The URI(s) of the Hive metastore to connect to using the
Thrift protocol. If multiple URIs are provided, the first
URI is used by default, and the rest of the URIs are
fallback metastores.
Example: |
|
|
The storage file format for Iceberg tables. The available
values are |
|
|
The compression codec to use when writing files. The
available values are |
|
|
The catalog type for Iceberg tables. The available values
are |
|
|
The catalog warehouse root path for Iceberg tables.
|
|
|
The number of Iceberg catalogs to cache. This property is
required if the |
|
|
The path(s) for Hadoop configuration resources.
|
|
|
The Maximum number of partitions handled per writer. |
|
|
A decimal value in the range (0, 1] is used as a minimum for weights assigned to each split. A low value may improve performance on tables with small files. A higher value may improve performance for queries with highly skewed aggregations or joins. |
|
|
Enable reading base tables that use merge-on-read for updates. The Iceberg connector currently does not read delete lists, which means any updates will not be reflected in the table. |
|
Table Properties#
Table properties set metadata for the underlying tables. This is key for CREATE TABLE/CREATE TABLE AS statements. Table properties are passed to the connector using a WITH clause:
CREATE TABLE tablename
WITH (
property_name = property_value,
...
)
The following table properties are available, which are specific to the Presto Iceberg connector:
Property Name |
Description |
|---|---|
|
Optionally specifies the format of table data files,
either |
|
Optionally specifies table partitioning. If a table
is partitioned by columns |
|
Optionally specifies the file system location URI for the table. |
|
Optionally specifies the format version of the Iceberg
specification to use for new tables, either |
The table definition below specifies format ORC, partitioning by columns c1 and c2,
and a file system location of s3://test_bucket/test_schema/test_table:
CREATE TABLE test_table (
c1 bigint,
c2 varchar,
c3 double
)
WITH (
format = 'ORC',
partitioning = ARRAY['c1', 'c2'],
location = 's3://test_bucket/test_schema/test_table')
)
SQL Support#
The Iceberg connector supports querying and manipulating Iceberg tables and schemas (databases). Here are some examples of the SQL operations supported by Presto :
CREATE SCHEMA#
Create a new Iceberg schema named web that will store tables in an
S3 bucket named my-bucket:
CREATE SCHEMA iceberg.web
WITH (location = 's3://my-bucket/')
CREATE TABLE#
Create a new Iceberg table named page_views in the web schema
that is stored using the ORC file format, partitioned by ds and
country:
CREATE TABLE iceberg.web.page_views (
view_time timestamp,
user_id bigint,
page_url varchar,
ds date,
country varchar
)
WITH (
format = 'ORC',
partitioning = ARRAY['ds', 'country']
)
Create an Iceberg table with Iceberg format version 2:
CREATE TABLE iceberg.web.page_views_v2 (
view_time timestamp,
user_id bigint,
page_url varchar,
ds date,
country varchar
)
WITH (
format = 'ORC',
partitioning = ARRAY['ds', 'country'],
format_version = '2'
)
Partition Column Transform#
Beyond selecting some particular columns for partitioning, you can use the transform functions and partition the table
by the transformed value of the column.
Available transforms in the Presto Iceberg connector include:
Bucket(partitions data into a specified number of buckets using a hash function)Truncate(partitions the table based on the truncated value of the field and can specify the width of the truncated value)
Create an Iceberg table partitioned into 8 buckets of equal sized ranges:
CREATE TABLE players (
id int,
name varchar,
team varchar
)
WITH (
format = 'ORC',
partitioning = ARRAY['bucket(team, 8)']
);
Create an Iceberg table partitioned by the first letter of the team field:
CREATE TABLE players (
id int,
name varchar,
team varchar
)
WITH (
format = 'ORC',
partitioning = ARRAY['truncate(team, 1)']
);
Note
Day, Month, Year, Hour partition column transform functions are not supported in Presto Iceberg
connector yet (#20570).
INSERT INTO#
Insert data into the page_views table:
INSERT INTO iceberg.web.page_views VALUES(TIMESTAMP '2023-08-12 03:04:05.321', 1, 'https://example.com', current_date, 'country');
CREATE TABLE AS SELECT#
Create a new table page_views_new from an existing table page_views:
CREATE TABLE iceberg.web.page_views_new AS SELECT * FROM iceberg.web.page_views
SELECT#
SELECT table operations are supported for Iceberg format version 1 and version 2 in the connector:
SELECT * FROM iceberg.web.page_views;
SELECT * FROM iceberg.web.page_views_v2;
Note
The SELECT operations on Iceberg Tables with format version 2 do not read the delete files and
remove the deleted rows as of now (#20492).
ALTER TABLE#
Alter table operations are supported in the connector:
ALTER TABLE iceberg.web.page_views ADD COLUMN zipcode VARCHAR;
ALTER TABLE iceberg.web.page_views RENAME COLUMN zipcode TO location;
ALTER TABLE iceberg.web.page_views DROP COLUMN location;
TRUNCATE#
The iceberg connector can delete all of the data from tables without
dropping the table from the metadata catalog using TRUNCATE TABLE.
TRUNCATE TABLE nation;
TRUNCATE TABLE;
SELECT * FROM nation;
nationkey | name | regionkey | comment
-----------+------+-----------+---------
(0 rows)
DROP TABLE#
Drop the table page_views
DROP TABLE iceberg.web.page_views
Dropping an Iceberg table with Hive Metastore and Glue catalogs only removes metadata from metastore.
Dropping an Iceberg table with Hadoop and Nessie catalogs removes all the data and metadata in the table.
DROP SCHEMA#
Drop a schema:
DROP SCHEMA iceberg.web
Schema Evolution#
Iceberg and Presto Iceberg connector support in-place table evolution, aka schema evolution, such as adding, dropping, and renaming columns. With schema evolution, users can evolve a table schema with SQL after enabling the Presto Iceberg connector.
Example Queries#
Let’s create an Iceberg table named ctas_nation, created from the TPCH nation table. The table has four columns: nationkey, name, regionkey, and comment.
USE iceberg.tpch;
CREATE TABLE IF NOT EXISTS ctas_nation AS (SELECT * FROM nation);
DESCRIBE ctas_nation;
Column | Type | Extra | Comment
-----------+---------+-------+---------
nationkey | bigint | |
name | varchar | |
regionkey | bigint | |
comment | varchar | |
(4 rows)
We can simply add a new column to the Iceberg table by using ALTER TABLE statement. The following query adds a new column named zipcode to the table.
ALTER TABLE ctas_nation ADD COLUMN zipcode VARCHAR;
DESCRIBE ctas_nation;
Column | Type | Extra | Comment
-----------+---------+-------+---------
nationkey | bigint | |
name | varchar | |
regionkey | bigint | |
comment | varchar | |
zipcode | varchar | |
(5 rows)
We can also rename the new column to another name, address:
ALTER TABLE ctas_nation RENAME COLUMN zipcode TO address;
DESCRIBE ctas_nation;
Column | Type | Extra | Comment
-----------+---------+-------+---------
nationkey | bigint | |
name | varchar | |
regionkey | bigint | |
comment | varchar | |
address | varchar | |
(5 rows)
Finally, we can delete the new column. The table columns will be restored to the original state.
ALTER TABLE ctas_nation DROP COLUMN address;
DESCRIBE ctas_nation;
Column | Type | Extra | Comment
-----------+---------+-------+---------
nationkey | bigint | |
name | varchar | |
regionkey | bigint | |
comment | varchar | |
(4 rows)
Time Travel#
Iceberg and Presto Iceberg connector support time travel via table snapshots
identified by unique snapshot IDs. The snapshot IDs are stored in the $snapshots
metadata table. We can rollback the state of a table to a previous snapshot ID.
Example Queries#
Similar to the example queries in Schema Evolution, let’s create an Iceberg table named ctas_nation, created from the TPCH nation table.
USE iceberg.tpch;
CREATE TABLE IF NOT EXISTS ctas_nation AS (SELECT * FROM nation);
DESCRIBE ctas_nation;
Column | Type | Extra | Comment
-----------+---------+-------+---------
nationkey | bigint | |
name | varchar | |
regionkey | bigint | |
comment | varchar | |
(4 rows)
We can find snapshot IDs for the Iceberg table from the $snapshots metadata table.
SELECT snapshot_id FROM iceberg.tpch."ctas_nation$snapshots" ORDER BY committed_at;
snapshot_id
---------------------
5837462824399906536
(1 row)
For now, as we’ve just created the table, there’s only one snapshot ID. Let’s insert one row into the table and see the change in the snapshot IDs.
INSERT INTO ctas_nation VALUES(25, 'new country', 1, 'comment');
SELECT snapshot_id FROM iceberg.tpch."ctas_nation$snapshots" ORDER BY committed_at;
snapshot_id
---------------------
5837462824399906536
5140039250977437531
(2 rows)
Now there’s a new snapshot (5140039250977437531) created as a new row is inserted into the table. The new row can be verified by running
SELECT * FROM ctas_nation WHERE name = 'new country';
nationkey | name | regionkey | comment
-----------+-------------+-----------+---------
25 | new country | 1 | comment
(1 row)
With the time travel feature, we can rollback to the previous state without the new row by calling iceberg.system.rollback_to_snapshot:
CALL iceberg.system.rollback_to_snapshot('tpch', 'ctas_nation', 5837462824399906536);
Now if we check the table again, we’ll find that the newly inserted row no longer exists as we’ve rolled back to the previous state.
SELECT * FROM ctas_nation WHERE name = 'new country';
nationkey | name | regionkey | comment
-----------+------+-----------+---------
(0 rows)
Iceberg Connector Limitations#
The
SELECToperations on Iceberg Tables with format version 2 do not read the delete files and remove the deleted rows as of now (#20492).